Mailman 3 April 2017 - scikit-image

Important change in testing environment
by Egor Panfilov 12 Apr '17

12 Apr '17

Dear contributors, We'd like to notify your that we have just finished a transition of our testing environment from `nose` to `pytest` (see https://github.com/scikit-image/scikit-image/pull/2468 for the details). We kindly ask you to revisit your open Pull Requests on GitHub, perform a rebase onto `master` branch of `scikit-image/scikit-image` repo (see http://scikit-image.org/docs/dev/contribute.html#divergence-between-upstrea…), and rewrite your tests using `pytest` wherever is needed. Usage of the functions from `numpy.testing` in addition to `pytest` ones is allowed, but, please, notice that some of them (such as `assert_raises`) rely on `nose` under the hood, and, therefore, should not be used. Sorry for the inconvenience. Looking forward to seeing your contributions updated and merged. Regards, Egor, scikit-image core team

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Non-rigid shape matching
by Surya Kasturi 11 Apr '17

11 Apr '17

Hey everyone, My name is Surya — working on a project on image classification. I’m interested in identifying non-rigid shapes (close to an ellipse) in an image. Sample image below. Could anyone please point me to some references for that? Thanks Surya

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Re: [scikit-image] Help with making moments nD
by Juan Nunez-Iglesias 08 Apr '17

08 Apr '17

Hi Jaime, Thanks for an outrageously thorough response! Firstly, I totally missed that the dc and dr multipliers were in a different order! Looks like we will have to do some deprecation! Second, my own timings match yours, even for the 2D horse image. I’ve renamed my function moments_central_np0 and yours _np[1-3]. Plain moments_central is the skimage implementation. In [216]: %timeit moments_central(horse, *central_coords, 3) 100 loops, best of 3: 3.11 ms per loop In [217]: %timeit m.moments_central_np0(horse, central_coords, 3) 10 loops, best of 3: 39.2 ms per loop In [218]: %timeit m.moments_central_np1(horse, central_coords, 3) 100 loops, best of 3: 6.49 ms per loop In [219]: %timeit m.moments_central_np2(horse, central_coords, 3) 100 loops, best of 3: 5.28 ms per loop In [220]: %timeit m.moments_central_np3(horse, central_coords, 3) The slowest run took 5.99 times longer than the fastest. This could mean that an intermediate result is being cached. 1000 loops, best of 3: 375 µs per loop In [221]: %timeit m.moments_central_np3(horse, central_coords, 3) 1000 loops, best of 3: 396 µs per loop The only thing I’m concerned about is that “intermediate result being cached”. Any idea why that could be? It only seems to happen with the third implementation…! Either way, even the slowest time is faster than the other functions! Anyway, thanks so much for your help! Juan. On 7 Apr 2017, 9:22 PM +1000, Jaime Fernández del Río <jaime.frio(a)gmail.com>, wrote: > Hi Juan, > > The transposition is almost certainly due to the fact that in the Cython implementation the p loop, which loops rows of the moments array, uses the dc multiplier, which comes from the image columns, while the q loop, which loops columns of the moments array, uses the dr multiplier, which comes from the image rows. Your other code chooses the multiplier corresponding to the same axes in both the image and the moments arrays. It is not obvious to me how to extend the current behavior to higher dimensions. > > As for your implementation, using np.nonzero is probably not going to pay off unless your image is very sparse, because you basically go from having to handle a single value per image pixel, to add an extra coordinate value for each dimension. And you don't really need to compute an offset for each pixel, since many pixels share the offsets along some dimensions, e.g. for a 100 x 100 x 100 3D image, this code is 2-3x faster: > > import itertools > > def moments_central(image, center=None, order=3): > image = np.asarray(image) > if center is None: > center = np.array(image.shape, dtype=float) / 2 > center = np.asarray(center) > assert center.ndim == 1 and center.size == image.ndim > # deltas is a list of arrays holding the deltas from the center along each > # dimension of the image. > deltas = [np.arange(image.shape[dim], dtype=float) - center[dim] > for dim in range(image.ndim)] > # Reshape each delta so that it can properly broadcast against image. > deltas = [delta.reshape((1,) * dim + (-1,) + (1,) * (image.ndim - dim - 1)) > for dim, delta in enumerate(deltas)] > moments = np.empty((order + 1,) * image.ndim, dtype=float) > reusable_buffer = np.empty_like(image, dtype=float) > for powers in itertools.product(range(order + 1), repeat=image.ndim): > reusable_buffer[:] = image > for power, delta in zip(powers, deltas): > reusable_buffer *= delta ** power > moments[tuple(powers)] = reusable_buffer.sum() > > return moments > > Notice that this just iterates over the moments items. Broadcasting in this type of problems is dangerous, because you basically replace an N item loop with creating an arrays N times larger than your original data. And Python loops are slow, but not THAT slow. You can actually make the above idea run even faster, almost 2x, by reducing the dimensionality of your result as soon as possible earlier: > > import itertools > > def moments_central_faster(image, center=None, order=3): > image = np.asarray(image) > if center is None: > center = np.array(image.shape, dtype=float) / 2 > center = np.asarray(center) > assert center.ndim == 1 and center.size == image.ndim > # deltas is a list of arrays holding the deltas from the center along each > # dimension of the image. > deltas = [np.arange(image.shape[dim], dtype=float) - center[dim] > for dim in range(image.ndim)] > # Reshape each delta so that it can properly broadcast against image. > deltas = [delta.reshape((1,) * dim + (-1,) + (1,) * (image.ndim - dim - 1)) > for dim, delta in enumerate(deltas)] > moments = np.empty((order + 1,) * image.ndim, dtype=float) > for powers in itertools.product(range(order + 1), repeat=image.ndim): > calc = image.copy() > for dim, (power, delta) in enumerate(zip(powers, deltas)): > calc *= delta ** power > calc = np.add.reduce(calc, axis=dim, keepdims=True) > moments[tuple(powers)] = calc.sum() > > return moments > > This idea of reducing dimensionality can be given a twist in the form of stacked matrix multiplication: one can think of the whole calculation as reducing each dimension of size D to O, where O is the order plus one, by multiplying by a DXO matrix, holding the powers of the deltas along that dimension. In code it could look something like this: > > def moments_central_even_faster(image, center=None, order=3): > image = np.asarray(image) > if center is None: > center = np.array(image.shape, dtype=float) / 2 > center = np.asarray(center) > assert center.ndim == 1 and center.size == image.ndim > # deltas is a list of arrays holding the deltas from the center along each > # dimension of the image. > deltas = [np.arange(image.shape[dim], dtype=float) - center[dim] > for dim in range(image.ndim)] > calc = image > for dim, delta in enumerate(deltas): > powers_of_delta = delta[:, None] ** np.arange(order + 1) > calc = np.rollaxis(calc, dim, image.ndim) > calc = np.dot(calc, powers_of_delta) > calc = np.rollaxis(calc, -1, dim) > > return calc > > This turns out to be ridiculously fast for the 100 x 100 x 100 image case (1.41 s vs 520 ms vs 258 ms vs 10.5 ms), so over 100x (!!!) faster than your implementation. > > On a 1000 x 1000 2D image it's fast, but not the fastest (571 ms vs 122 ms vs 92.8 ms vs 113 ms), but the fastest version is probably in the same ballpark as the current Cython implementation. > > Let me know how do your timings compare. > > Jaime > > > > > On Fri, Apr 7, 2017 at 9:16 AM, Juan Nunez-Iglesias <jni.soma(a)gmail.com> wrote: > > > Hi everyone, > > > > > > I’ve started work on getting image moments working in nD. I have two working alternative implementations: one with NumPy broadcasting and one with Numba JIT. Both are consistent with each other and with the existing Cython implementation, with to caveats: > > > > > > 1- my results are the *transpose* of the reference results. I suspected this might be because the reference uses x/y coordinates but that doesn’t seem to be the case. (see code below). > > > > > > 2- each of my implementations is ~10x slower than the Cython implementation. > > > > > > Of these, problem 1 is the most pressing. (Problem 2 can be solved by special-casing 2D.) Can someone familiar with moments compare my implementations and the reference and tell me where I might have gone wrong? I’ve included all the code below for reference. > > > > > > Ideas about speedups are also welcome! To that end, I’ve marked the slow lines in the broadcasting code. (I don’t know how to line profile in Numba; I think it’s currently impossible, in fact.) > > > > > > Juan. > > > > > > ————————— Reference results > > > > > > ———————————— Values > > > > > > (Notice the .T for `moments_central`, which is the existing Cython implementation) > > > > > > In [189]: m.moments_central_nb(horse.astype(float), central_coords, 3) > > > Out[189]: > > > array([[ 8.779e+04, -1.621e-07, 1.301e+09, -6.370e+09], > > > [ -8.245e-07, 9.275e+07, -3.430e+09, 1.938e+12], > > > [ 9.873e+08, -1.363e+10, 1.349e+13, -3.604e+14], > > > [ -2.218e+10, 1.552e+12, -2.849e+14, 3.245e+16]]) > > > > > > In [190]: moments_central(horse.astype(float), *central_coords, 3).T > > > Out[190]: > > > array([[ 8.779e+04, -1.621e-07, 1.301e+09, -6.370e+09], > > > [ -8.245e-07, 9.275e+07, -3.430e+09, 1.938e+12], > > > [ 9.873e+08, -1.363e+10, 1.349e+13, -3.604e+14], > > > [ -2.218e+10, 1.552e+12, -2.849e+14, 3.245e+16]]) > > > > > > In [191]: m.moments_central(horse.astype(float), central_coords, 3) > > > Out[191]: > > > array([[ 8.779e+04, -1.037e-09, 1.301e+09, -6.370e+09], > > > [ -1.979e-09, 9.275e+07, -3.430e+09, 1.938e+12], > > > [ 9.873e+08, -1.363e+10, 1.349e+13, -3.604e+14], > > > [ -2.218e+10, 1.552e+12, -2.849e+14, 3.245e+16]]) > > > > > > ———————————— Timings > > > > > > In [185]: %timeit moments_central(horse.astype(float), *central_coords, 3) > > > 100 loops, best of 3: 3.14 ms per loop > > > > > > In [187]: %timeit m.moments_central_nb(horse.astype(float), central_coords, 3) > > > 10 loops, best of 3: 35.7 ms per loop > > > > > > In [188]: %timeit m.moments_central(horse.astype(float), central_coords, 3) > > > 10 loops, best of 3: 39.4 ms per loop > > > > > > ————————— Implementations > > > > > > ———————————— Existing Cython > > > cpdef moments_central(image_t image, double cr, double cc, Py_ssize_t order): > > > cdef Py_ssize_t p, q, r, c > > > cdef double[:, ::1] mu = np.zeros((order + 1, order + 1), dtype=np.double) > > > cdef double val, dr, dc, dcp, drq > > > for r in range(image.shape[0]): > > > dr = r - cr > > > for c in range(image.shape[1]): > > > dc = c - cc > > > val = image[r, c] > > > dcp = 1 > > > for p in range(order + 1): > > > drq = 1 > > > for q in range(order + 1): > > > mu[p, q] += val * drq * dcp > > > drq *= dr > > > dcp *= dc > > > return np.asarray(mu) > > > > > > ———————————— Broadcasting > > > import numpy as np > > > import numba > > > from functools import reduce > > > > > > def moments_central(image, center=None, order=3): > > > coords = np.nonzero(image) > > > values = image[coords] > > > coords_stack = np.column_stack(np.nonzero(image)) > > > if center is None: > > > center = np.mean(coords_stack, axis=0) > > > ccoords = coords_stack - center > > > powers = np.arange(order + 1) > > > result_shape = (order + 1,) * image.ndim + (coords_stack.shape[0],) > > > coords_pow = [] > > > for dim in range(image.ndim): > > > power_shape = [1,] * (image.ndim + 1) > > > power_shape[dim] = order + 1 > > > powers = np.reshape(powers, power_shape) > > > ccoords_pow = ccoords[:, dim] ** powers # SLOW, 50% of time > > > bcast_coords_pow = np.broadcast_to(ccoords_pow, result_shape) > > > coords_pow.append(bcast_coords_pow) > > > result = np.sum(reduce(np.multiply, coords_pow + [values]), axis=-1) # SLOW, 30% of time > > > return result > > > > > > ———————————— Numba > > > def moments_central_nb(image, center=None, order=3): > > > mu = np.zeros((order + 1,) * image.ndim) > > > if center is None: > > > center = np.mean(np.column_stack(np.nonzero(image)), axis=0) > > > _moments_central_nb_loop(image, center, mu) > > > return mu > > > > > > > > > @numba.njit > > > def _moments_central_nb_loop(image, center, mu): > > > for coord, val in np.ndenumerate(image): > > > if val > 0: > > > for powers in np.ndindex(mu.shape): > > > curval = val > > > for c, ctr, p in zip(coord, center, powers): > > > dc = c - ctr > > > cr = 1 > > > for pcount in range(p): > > > cr *= dc > > > curval *= cr > > > mu[powers] += curval > > > > > > > > > > > > > > > _______________________________________________ > > > scikit-image mailing list > > > scikit-image(a)python.org > > > https://mail.python.org/mailman/listinfo/scikit-image > > > > > > > -- > (\__/) > ( O.o) > ( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus planes de dominación mundial.

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Help with making moments nD
by Juan Nunez-Iglesias 07 Apr '17

07 Apr '17

Hi everyone, I’ve started work on getting image moments working in nD. I have two working alternative implementations: one with NumPy broadcasting and one with Numba JIT. Both are consistent with each other and with the existing Cython implementation, with to caveats: 1- my results are the *transpose* of the reference results. I suspected this might be because the reference uses x/y coordinates but that doesn’t seem to be the case. (see code below). 2- each of my implementations is ~10x slower than the Cython implementation. Of these, problem 1 is the most pressing. (Problem 2 can be solved by special-casing 2D.) Can someone familiar with moments compare my implementations and the reference and tell me where I might have gone wrong? I’ve included all the code below for reference. Ideas about speedups are also welcome! To that end, I’ve marked the slow lines in the broadcasting code. (I don’t know how to line profile in Numba; I think it’s currently impossible, in fact.) Juan. ————————— Reference results ———————————— Values (Notice the .T for `moments_central`, which is the existing Cython implementation) In [189]: m.moments_central_nb(horse.astype(float), central_coords, 3) Out[189]: array([[ 8.779e+04, -1.621e-07, 1.301e+09, -6.370e+09], [ -8.245e-07, 9.275e+07, -3.430e+09, 1.938e+12], [ 9.873e+08, -1.363e+10, 1.349e+13, -3.604e+14], [ -2.218e+10, 1.552e+12, -2.849e+14, 3.245e+16]]) In [190]: moments_central(horse.astype(float), *central_coords, 3).T Out[190]: array([[ 8.779e+04, -1.621e-07, 1.301e+09, -6.370e+09], [ -8.245e-07, 9.275e+07, -3.430e+09, 1.938e+12], [ 9.873e+08, -1.363e+10, 1.349e+13, -3.604e+14], [ -2.218e+10, 1.552e+12, -2.849e+14, 3.245e+16]]) In [191]: m.moments_central(horse.astype(float), central_coords, 3) Out[191]: array([[ 8.779e+04, -1.037e-09, 1.301e+09, -6.370e+09], [ -1.979e-09, 9.275e+07, -3.430e+09, 1.938e+12], [ 9.873e+08, -1.363e+10, 1.349e+13, -3.604e+14], [ -2.218e+10, 1.552e+12, -2.849e+14, 3.245e+16]]) ———————————— Timings In [185]: %timeit moments_central(horse.astype(float), *central_coords, 3) 100 loops, best of 3: 3.14 ms per loop In [187]: %timeit m.moments_central_nb(horse.astype(float), central_coords, 3) 10 loops, best of 3: 35.7 ms per loop In [188]: %timeit m.moments_central(horse.astype(float), central_coords, 3) 10 loops, best of 3: 39.4 ms per loop ————————— Implementations ———————————— Existing Cython cpdef moments_central(image_t image, double cr, double cc, Py_ssize_t order): cdef Py_ssize_t p, q, r, c cdef double[:, ::1] mu = np.zeros((order + 1, order + 1), dtype=np.double) cdef double val, dr, dc, dcp, drq for r in range(image.shape[0]): dr = r - cr for c in range(image.shape[1]): dc = c - cc val = image[r, c] dcp = 1 for p in range(order + 1): drq = 1 for q in range(order + 1): mu[p, q] += val * drq * dcp drq *= dr dcp *= dc return np.asarray(mu) ———————————— Broadcasting import numpy as np import numba from functools import reduce def moments_central(image, center=None, order=3): coords = np.nonzero(image) values = image[coords] coords_stack = np.column_stack(np.nonzero(image)) if center is None: center = np.mean(coords_stack, axis=0) ccoords = coords_stack - center powers = np.arange(order + 1) result_shape = (order + 1,) * image.ndim + (coords_stack.shape[0],) coords_pow = [] for dim in range(image.ndim): power_shape = [1,] * (image.ndim + 1) power_shape[dim] = order + 1 powers = np.reshape(powers, power_shape) ccoords_pow = ccoords[:, dim] ** powers # SLOW, 50% of time bcast_coords_pow = np.broadcast_to(ccoords_pow, result_shape) coords_pow.append(bcast_coords_pow) result = np.sum(reduce(np.multiply, coords_pow + [values]), axis=-1) # SLOW, 30% of time return result ———————————— Numba def moments_central_nb(image, center=None, order=3): mu = np.zeros((order + 1,) * image.ndim) if center is None: center = np.mean(np.column_stack(np.nonzero(image)), axis=0) _moments_central_nb_loop(image, center, mu) return mu @numba.njit def _moments_central_nb_loop(image, center, mu): for coord, val in np.ndenumerate(image): if val > 0: for powers in np.ndindex(mu.shape): curval = val for c, ctr, p in zip(coord, center, powers): dc = c - ctr cr = 1 for pcount in range(p): cr *= dc curval *= cr mu[powers] += curval

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How to move pull requests forward (devs, please read)
by Stefan van der Walt 07 Apr '17

07 Apr '17

Hi everyone, and especially devs This afternoon, Nelle Varoquaux gave a short presentation at BIDS on optimistic merging, a workflow suggested by the late Pieter Hintjens. In optimistic merging, all PRs are merged into a project, and fixes happen via additional patches or reverts. While, in my mind, this is not a viable model for scikit-image, it made me think of our PR review process. A failure mode I observe often is the following: - user submits patch - review happens, often either a) nitpicking on minor things or b) asking for technically difficult tasks to be performed (rebasing, can be tricky) This introduces several problems, including discouraging new contributors, and wasting a lot of time (many PRs are left hanging for months, while waiting for a one-line fix from the original author). I'd like to request the following: - Ask contributors to open their branches for collaboration (this is on by default already, probably). - While reviewing a PR, pull down the branch, look at the diff, and fix any trivial errors you see. Push the results back up or, if collaboration is switched off, make a PR against theirs. - Keep review comments to bigger picture changes, and avoid getting bogged down in trivial fixes and stylistic changes. Let's keep it collaborative and friendly, grow our team of contributors, and get going on 0.14 :) Thanks! Stéfan P.S. A question for the git experts: I've been looking for ways of making the above process easier. I found that I can easily download pull requests: - In my ~/.gitconfig, I add the following alias (replace 'upstream' with whatever you normally call the scikit-image main repository): [alias] pullpr = "!f() { git fetch upstream pull/$1/head:pr$1 ;}; f" I can now grab a PR with: git pullpr 2447 and it will appear in the branch pr2447. Unfortunately, I don't have a similarly easy way of pushing back, other than adding the contributor's remote, and pushing back to the PR branch. Any ideas?

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Re: [scikit-image] How to see full image after transformation not the cropped one
by Stefan van der Walt 06 Apr '17

06 Apr '17

On Thu, Apr 6, 2017, at 09:57, Stefan van der Walt wrote: > On Wed, Apr 5, 2017, at 19:02, Serge Shakhov wrote: >> Just want to clarify. Am I right that your solution isn't applicable >> to PiecewiseAffineTransform at all? >> Do you think it may be possible to find a workaround? > > Sorry, I should have been clearer. With "defined" I meant > implemented. Upon closer inspection, it turns out that the PiecewiseAffineTransform *does* have an inverse implemented. It was the "+" operator causing the problem. Still, I have found cases now where I get invalid answers for valid- seeming sets of coordinates. I'm investigating further, but I think I have a solution to your problem. Stéfan

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Re: [scikit-image] How to see full image after transformation not the cropped one
by Serge Shakhov 06 Apr '17

06 Apr '17

>>To invert a piecewise affine transform, you'll have to use SciPy KD-tree to find the nearest quad coordinates, and then invert the matching affine. This is not necessarily hard, but it's not trivial to implement. Definitely too hard for a beginner :-) Probably I should just increase image canvas size to have empty area cropped and the picture to remain full. Thanks. Best regards, Serge Shakhov. On Friday, 7 April 2017, 4:57, Stefan van der Walt <stefanv(a)berkeley.edu> wrote: On Wed, Apr 5, 2017, at 19:02, Serge Shakhov wrote: Just want to clarify. Am I right that your solution isn't applicable to PiecewiseAffineTransform at all? Do you think it may be possible to find a workaround? Sorry, I should have been clearer. With "defined" I meant implemented. To invert a piecewise affine transform, you'll have to use SciPy KD-tree to find the nearest quad coordinates, and then invert the matching affine. This is not necessarily hard, but it's not trivial to implement. Stéfan

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Re: [scikit-image] Tools for 3D segmentation ?
by Stefan van der Walt 06 Apr '17

06 Apr '17

If anyone is willing to write an overview of how to do this with screenshots, that'd make a great addition to the website. Stéfan On Wed, Apr 5, 2017, at 18:57, Vighnesh Birodkar wrote: > Hey guys > > Sorry for not being clear about this. What I meant is manual labelling > of 3D volumes. I cannot reveal all the details, but it's basically a > foreground Vs background labelling task. > > On 5 Apr 2017 9:52 pm, "Juan Nunez-Iglesias" > <jni.soma(a)gmail.com> wrote: >> Well, almost three years ago we had a GSoC student work on it… =P >> >> Assuming you mean manual 3D segmentation, I second Greg’s >> recommendation of ITK-SNAP for fully manual segmentation. For semi- >> automated work, I recommend Ilastik (ilastik.org). >> >> Juan. >> >> On 5 Apr 2017, 7:43 PM -0400, Gregory Lee <grlee77(a)gmail.com>, wrote: >> >>> >>>> I was wondering if anyone on the list knew about software to do 3d >>>> segmentation. >>>> >>>> Thanks >>>> Vighnesh >>>> >>> >>> A nice, free GUI-based tool for interactive 3D segmentation is >>> itk-snap >>> http://www.itksnap.org/ >>> >>> _______________________________________________ >>> scikit-image mailing list >>> scikit-image(a)python.org >>> https://mail.python.org/mailman/listinfo/scikit-image > _________________________________________________ > scikit-image mailing list > scikit-image(a)python.org > https://mail.python.org/mailman/listinfo/scikit-image

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Re: [scikit-image] How to see full image after transformation not the cropped one
by Serge Shakhov 06 Apr '17

06 Apr '17

okay, but I don't need Affine.Does it mean that there is no solution for my problem? Best regards, Serge Shakhov. On Wednesday, 5 April 2017, 10:35, Stefan van der Walt <stefanv(a)berkeley.edu> wrote: Hi Serge There is no inverse defined for the piecewise affine, hence the change I made. Stéfan On Tue, Apr 4, 2017, at 15:28, Serge Shakhov wrote: Hi Stefan. Noticed that you changed my PiecewiseAffineTransform to AffineTransform. Is it applicable to PiecewiseAffineTransform? When I tried to revert it back to PiecewiseAffineTransform Got error: Traceback (most recent call last): File "C:/Users/Serge/Downloads/warp_to_extents.py", line 45, in <module> shifted_transform = (affine + offset).inverse File "C:\Users\Serge\Software\lib\site-packages\skimage\transform\_geometric.py", line 208, in __add__ raise NotImplementedError() NotImplementedError Thanks Best regards, Serge Shakhov. On Tuesday, 4 April 2017, 18:58, Stefan van der Walt <stefanv(a)berkeley.edu> wrote: Hi Serge Have a look at the attached version. Stéfan

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Re: [scikit-image] Tools for 3D segmentation ?
by Juan Nunez-Iglesias 06 Apr '17

06 Apr '17

Well, almost three years ago we had a GSoC student work on it… =P Assuming you mean manual 3D segmentation, I second Greg’s recommendation of ITK-SNAP for fully manual segmentation. For semi-automated work, I recommend Ilastik (ilastik.org). Juan. On 5 Apr 2017, 7:43 PM -0400, Gregory Lee <grlee77(a)gmail.com>, wrote: > > > > I was wondering if anyone on the list knew about software to do 3d segmentation. > > > > > > Thanks > > > Vighnesh > > > > > > > A nice, free GUI-based tool for interactive 3D segmentation is itk-snap > > http://www.itksnap.org/ > > > _______________________________________________ > scikit-image mailing list > scikit-image(a)python.org > https://mail.python.org/mailman/listinfo/scikit-image

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